tipos de atrasos mais frequentes:
- atraso menstrual
- atrasos cp
- atrasos comboios cp
- atrasos aeroporto lisboa
- atrasos ctt
tipos de atrasos mais frequentes:
Hadoop: We have multiple clusters storing over 500 PB divided in four groups (real time, processing, data warehouse and cold storage). Our biggest cluster is over 10k nodes. We run 150k applications and launch 130M containers per day.
Manhattan(the backend for Tweets, Direct Messages, Twitter accounts, and more): We run several clusters for different use cases such as large multi tenant, smaller for non common, read only, and read/write for heavy write/heavy read traffic patterns. The read/only cluster handles 10s of millions QPS whereas a read/write cluster handles millions of QPS. The highest performance cluster, our observability cluster, which ingests in every datacenter, handles over tens of million writes.
Graph: Our legacy Gizzard/MySQL based sharded cluster for storing our graphs. Flock, our social graph, can manage peaks over tens of million QPS, averaging our MySQL servers to 30k – 45k QPS.
Blobstore: Our image, video and large file store where we store hundreds of billions objects.
Cache: Our Redis and Memcache clusters: caching our users, timelines, tweets and more.
SQL: This includes MySQL, PostgreSQL and Vertica. MySQL/PosgreSQL are used where we need strong consistency, managing ads campaign, ads exchange as well as internal tools. Vertica is a column store often used as a backend for Tableau supporting sales and user organisations.
Overview of Twitter Fleet
Twitter came of age when hardware from physical enterprise vendors ruled the data center. Since then we’ve continually engineered and refreshed our fleet to take advantage of the latest open standards in technology and hardware efficiency in order to deliver the best possible experience.
Google Has Quietly Dropped Ban on Personally Identifiable Web Tracking
Google is the latest tech company to drop the longstanding wall between anonymous online ad tracking and user’s names.
FBI whistleblower Jesselyn Radack joins RT America’s Simone Del Rosario to discuss thye growing concern that the NSA is collecting so much data that it can no longer be effective in preventing terror. Radack says the terror attacks of 9/11 created a ‘blank check’ wherein the usual constraints on surveillance were removed, including probable cause and the necessity of getting a warrant before conducting domestic data collection.
Microsoft Back Doors
- Microsoft Windows has a universal back door through which any change whatsoever can be imposed on the users.More information on when this was used.
In Windows 10, the universal back door is no longer hidden; all “upgrades” will be forcibly and immediately imposed.
- Windows 8 also has a back door for remotely deleting apps.You might well decide to let a security service that you trust remotely deactivate programs that it considers malicious. But there is no excuse for deleting the programs, and you should have the right to decide who (if anyone) to trust in this way.
- Windows 8’s back doors are so gaping that the German government has decided it can’t be trusted.
The wrongs in this section are not precisely malware, since they do not involve making the program that runs in a way that hurts the user. But they are a lot like malware, since they are technical Microsoft actions that harm to the users of specific Microsoft software.
- Microsoft is repeatedly nagging many users to install Windows 10.
- Microsoft informs the NSA of bugs in Windows before fixing them.
- Microsoft cut off security fixes for Windows XP, except to some big users that pay exorbitantly.Microsoft is going to cut off support for some Internet Explorer versions in the same way.
A person or company has the right to cease to work on a particular program; the wrong here is Microsoft does this after having made the users dependent on Microsoft, because they are not free to ask anyone else to work on the program for them.
- Windows 10 ships with default settings that show no regard for the privacy of its users, giving Microsoft the “right” to snoop on the users’ files, text input, voice input, location info, contacts, calendar records and web browsing history, as well as automatically connecting the machines to open hotspots and showing targeted ads.
- Windows 10 sends identifiable information to Microsoft, even if a user turns off its Bing search and Cortana features, and activates the privacy-protection settings.
The unique “advertising ID” for each user enables other companies to track the browsing of each specific user.
It’s as if Microsoft has deliberately chosen to make Windows 10 maximally evil on every dimension; to make a grab for total power over anyone that doesn’t drop Windows now.
- Windows 10 requires users to give permission for total snooping, including their files, their commands, their text input, and their voice input.
- Spyware in Windows: Windows Update snoops on the user. Windows 8.1 snoops on local searches. And there’s a secret NSA key in Windows, whose functions we don’t know.
- Microsoft SkyDrive allows the NSA to directly examine users’ data.
- DRM (digital restrictions mechanisms) in Windows, introduced to cater to Bluray disks. (The article also talks about how the same malware would later be introduced in MacOS.)
- Windows 8 on “mobile devices” is a jail: it censors the user’s choice of application programs.
- Mobile devices that come with Windows 8 are tyrants: they block users from installing other or modified operating systems.
As this page shows, if you do want to clean your computer of malware, the first software to delete is Windows.
Screencast Building a Fast Data Front End for Hadoop.
Date: This event took place live on June 24 2015
Presented by: John Hugg
Duration: Approximately 60 minutes.
Massive increases in both the volume and velocity of data have led to the development of interactive, real-time applications on fast streaming data. These fast data applications are often the front ends to Big Data (data at rest) and require integration between Fast + Big. To provide maximum value they require a data pipeline with the ability to compute real-time analytics on fast moving data, to make transactional decisions against state, and ultimately deliver data at high speeds to long-term Hadoop-based analytics stores like Cloudera, Hortonworks and MapR.
The new challenge is building applications that tap fast data and seamlessly integrate with the value contained in data stores — combining machine learning and dynamic processing. A variety of approaches are employed including Apache Storm, Spark Streaming, Samza, and in-memory database technology.
During this webcast you will learn:
- The pros and cons of the various approaches used to create fast data applications
- The pros and cons of Lambda and Kappa architectures compared to more traditional approaches
- Understand the tradeoffs and advantages surrounding the resurgence of ACID and SQL
- How integration with the Hadoop ecosystem can reduce latency and improve transactional intelligence
About John Hugg, Founding Engineer
John Hugg is a Software Developer at VoltDB. He has spent his entire career working with databases and information management. In 2008, he was lured away from a Ph.D. program by Mike Stonebraker to work on what became VoltDB. As the first engineer on the product, he liaised with a team of academics at MIT, Yale, and Brown who were building H-Store, VoltDB’s research prototype. Then he helped build the world-class engineering team at VoltDB to continue development of the open source and commercial products.